Surveillance system and method for predicting patient falls using motion feature patterns

ABSTRACT

A method and system for detecting a fall risk condition, the system comprising a surveillance camera configured to generate a plurality of frames showing an area in which a patient at risk of falling is being monitored, and a computer system comprising memory and logic circuitry configured to store motion feature patterns that are extracted from video recordings, the motion feature patterns are representative of motion associated with real alarm cases and false-alarm cases of fall events, receive a fall alert from a classifier, determine motion features of one or more frames from the plurality of frames that correspond to the fall alert; compare the motion features of the one or more frames with the motion feature patterns, and determine whether to confirm the fall alert based on the comparison.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. patent applicationSer. No. 16/353,485, entitled “SURVEILLANCE SYSTEM AND METHOD FORPREDICTING PATIENT FALLS USING MOTION FEATURE PATTERNS,” filed on Mar.14, 2019, which is a continuation of U.S. patent application Ser. No.16/043,965, entitled “SURVEILLANCE SYSTEM AND METHOD FOR PREDICTINGPATIENT FALLS USING MOTION FEATURE PATTERNS,” filed on Jul. 24, 2018,issued as U.S. Pat. No. 10,276,019, which is a continuation of U.S.patent application Ser. No. 15/824,552, entitled “SURVEILLANCE SYSTEMAND METHOD FOR PREDICTING PATIENT FALLS USING MOTION FEATURE PATTERNS,”filed on Nov. 28, 2017, issued as U.S. Pat. No. 10,055,961, which claimsthe priority of U.S. Provisional Application No. 62/530,380, entitled“System and Method for Predicting Patient Falls,” filed on Jul. 10,2017.

The present application is related to the following patents andapplications, which are assigned to the assignee of the presentinvention:

-   -   U.S. Pat. No. 7,477,285, filed Dec. 12, 2003, entitled        “Non-intrusive Data Transmission Network for Use in an        Enterprise Facility and Method for Implementing,”    -   U.S. Pat. No. 7,987,069, filed Nov. 11, 2008, entitled        “Monitoring Patient Support Exiting and Initiating Response,”    -   U.S. Pat. No. 8,471,899, filed Oct. 27, 2009, entitled “System        and Method for Documenting Patient Procedures,”    -   U.S. Pat. No. 8,675,059, filed Jul. 29, 2010, entitled “System        and Method for Using a Video Monitoring System to Prevent and        Manage Decubitus Ulcers in Patients,”    -   U.S. Pat. No. 8,676,603, filed Jun. 21, 2013, entitled “System        and Method for Documenting Patient Procedures,”    -   U.S. Pat. No. 9,041,810, filed Jul. 1, 2014, entitled “System        and Method for Predicting Patient Falls,”    -   U.S. Pat. No. 9,311,540, filed May 6, 2008, entitled “System and        Method for Predicting Patient Falls,”    -   U.S. Pat. No. 9,318,012, filed Mar. 23, 2012, entitled “Noise        Correcting Patient Fall Risk State System and Method for        Predicting Patient Falls,”    -   U.S. Pat. No. 9,579,047, filed Mar. 14, 2014, entitled “Systems        and Methods for Dynamically Identifying a Patient Support        Surface and Patient Monitoring,”    -   U.S. Pat. No. 9,635,320, filed May 12, 2015, entitled        “Electronic Patient Sitter Management System and Method for        Implementing,”    -   U.S. application Ser. No. 14/039,931, filed Sep. 27, 2013,        entitled “System and Method for Monitoring a Fall State of a        Patient While Minimizing False Alarms,”    -   U.S. application Ser. No. 13/714,587, filed Dec. 14, 2012,        entitled “Electronic Patient Sitter Management System and Method        for Implementing,”    -   U.S. application Ser. No. 14/158,016, filed Jan. 17, 2014,        entitled “Patient Video Monitoring Systems and Methods having        Detection Algorithm Recovery from Changes in Illumination,” and    -   U.S. application Ser. No. 15/364,872, entitled “System and        Method for Predicting Patient Falls,” filed on Nov. 30, 2016.

The above identified patents and applications are incorporated byreference herein in their entirety.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains material,which is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent files or records, but otherwise reserves all copyrightrights whatsoever.

BACKGROUND OF THE INVENTION

The invention described herein generally relates to a patient monitor,and in particular, a system, method and software program product foranalyzing video frames of a patient and determining from motion withinthe frame if the patient is at risk of a fall.

Fall reduction has become a major focus of all healthcare facilities,including those catering to permanent residents. Healthcare facilitiesinvest a huge amount of their resources in falls management programs andassessing the risk of falls in a particular patient class, location, andcare state, along with the risk factors associated with significantinjuries. Round the clock patient monitoring by a staff nurse isexpensive, therefore, healthcare facilities have investigatedalternatives in order to reduce the monitoring staff, while increasingpatient safety. Healthcare facilities rely on patient monitoring tosupplement interventions and reduce the instances of patient falls.

Many patient rooms now contain video surveillance equipment formonitoring and recording activity in a patient's room. Typically, thesevideo systems compare one video frame with a preceding frame for changesin the video frames that exceed a certain threshold level. More advancedsystems identify particular zones within the patient room that areassociated with a potential hazard for the patient. Then, sequentialvideo frames are evaluated for changes in those zones. Various systemsand methods for patient video monitoring have been disclosed in commonlyowned U.S. Patent Application Nos. 2009/0278934 entitled System andMethod for Predicting Patient Falls, 2010/0134609 entitled System andMethod for Documenting Patient Procedures, and 2012/0026308 entitledSystem and Method for Using a Video Monitoring System to Prevent andManage Decubitus Ulcers in Patients, each of which is incorporatedherein by reference in its entirety.

Such automated systems may be susceptible to false-alarms, which canburden a staff of healthcare professionals with unnecessaryinterventions. For example, a false-alarm can be triggered by patientactivity that is not indeed indicative of an increased risk of a patientfall. A false-alarm can also be triggered by the activity of a visitor(e.g., healthcare professional, family of patient) around the patient.While the aforementioned systems is capable of detecting potential fallsusing image processing techniques, there currently exists opportunitiesto improve the accuracy of such systems to reduce the number of falsepositives detected by such systems.

The inventions disclosed herein improve upon the previously discussedsystems for identifying and analyzing video frames to detect potentialfalls by employing supervised learning techniques to improve theaccuracy of fall detection given a plurality of video frames.Specifically, the present disclosure discusses techniques for analyzinga set of key features that indicate when a fall is about to occur. Byidentifying key features, the present disclosure may utilize a number ofsupervised learning approaches to more accurately predict the fall riskof future video frames.

Embodiments of invention disclosed herein provide numerous advantagesover existing techniques of analyzing image frame data to detect falls.As an initial improvement, the use of multiple image frames correctstraining data to remove noise appearing due to changes in lighting.During testing, the use of a classifier, versus more simplisticcomparison, yield at an accuracy level of approximately 92%. Thus, theembodiments of the disclosed invention offer significantly improvedperformance over existing techniques in standard conditions, whilemaintaining a consistent increase in performance in sub-optimalconditions (e.g., dim or no lighting).

SUMMARY OF THE INVENTION

The present invention provides a method and system for detecting a fallrisk condition. The system comprises a surveillance camera configured togenerate a plurality of frames showing an area in which a patient atrisk of falling is being monitored, and a computer system comprisingmemory and logic circuitry configured to store motion feature patternsthat are extracted from video recordings, the motion feature patternsare representative of motion associated with real alarm cases andfalse-alarm cases of fall events, receive a fall alert from aclassifier, the fall alert associated with the plurality of framesgenerated by the surveillance camera, determine motion features of oneor more frames from the plurality of frames that correspond to the fallalert; compare the motion features of the one or more frames with themotion feature patterns, and determine whether to confirm the fall alertbased on the comparison.

According to one embodiment, configuring the computer system to storethe motion feature patterns further comprises the computer systemconfigured to identify the real alarm cases or false-alarm cases of fallevents from the video recordings. In another embodiment, configuring thecomputer system to compare the motion features with the motion featurepatterns further includes the computer system configured to determinestatistically significant similarities between motion features of thefall alert and the stored motion feature patterns. Configuring thecomputer system to determine motion features of the one or more framesmay further comprise the computer system configured to detect motion ofpixels by comparing pixels of a current frame with at least one previousframe and mark pixels that have changed as a motion pixel in a givenmotion image.

The computer system may be further configured to identify the real alarmcases and false-alarm cases of fall events from the plurality of frames,and determine the motion feature patterns, wherein the motion featurepatterns are selected from the group consisting of a centroid, centroidarea, connected components ratio, bed motion percentage, and unconnectedmotion. The centroid may be located by further configuring the computersystem to compute weighted average x and y coordinates of motion pixelsin a given motion image. In one embodiment, the bed motion percentage isa ratio of motion pixels from a given motion image within the virtualbed zone to a total pixel count in the virtual bed zone. Configuring thecomputer system to determine the motion feature patterns may furthercomprise configuring the computer system to group motion pixels that areconnected in a given motion image into clusters and prune away motionpixels from the given motion image that don't have at least one pixelwithin a threshold distance of the virtual bed zone. A furtherembodiment includes the computer system configured to determine theconnected components ratio based on a ratio of motion pixels outside thevirtual bed zone to motion pixels inside the virtual bed zone. In yetanother embodiment, the computer system is further configured todetermine the unconnected motion by calculating an amount of motionpixels in the area of the centroid that is unrelated to connected motionpixels within and near the virtual bed zone.

The method comprises storing motion feature patterns that are extractedfrom video recordings, the motion feature patterns are representative ofmotion associated with real alarm cases and false-alarm cases of fallevents. The method further comprises receiving a fall alert from aclassifier, the fall alert associated with the plurality of framesgenerated by the surveillance camera, determining motion features of oneor more frames from the plurality of frames that correspond to the fallalert, comparing the motion features of the one or more frames with themotion feature patterns, and determining whether to confirm the fallalert based on the comparison.

According to one embodiment, storing the motion feature patterns furthercomprises identifying the real alarm cases or false-alarm cases of fallevents from the video recordings. Comparing the motion features with themotion feature patterns may further comprise determining statisticallysignificant similarities between motion features of the fall alert andthe stored motion feature patterns. Determining motion features of theone or more frames may further comprise detecting motion of pixels bycomparing pixels of a current frame with at least one previous frame andmarking pixels that have changed as a motion pixel in a given motionimage.

In one embodiment, the method may further comprise identifying realalarm cases and false-alarm cases from the plurality of frames, anddetermining motion feature patterns associated with the real alarm casesand false-alarm cases, the features including a centroid, centroid area,a connected components ratio, bed motion percentage, and unconnectedmotion, storing the motion feature patterns. The centroid may be locatedby computing weighted average x and y coordinates of motion pixels in agiven motion image. The bed motion percentage may be determined as aratio of motion pixels from a given motion image within the virtual bedzone to a total pixel count in the virtual bed zone. In one embodiment,determining the motion feature patterns may further comprise groupingmotion pixels that are connected in a given motion image into clustersand pruning away motion pixels from the given motion image that don'thave at least one pixel within a threshold distance of the virtual bedzone. The connected components ratio may be determined based on a ratioof motion pixels outside the virtual bed zone to motion pixels insidethe virtual bed zone. According to another embodiment, the methodfurther comprises determining the unconnected motion by calculating anamount of motion pixels in the area of the centroid that is unrelated toconnected motion pixels within and near the virtual bed zone.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention is illustrated in the figures of the accompanying drawingswhich are meant to be exemplary and not limiting, in which likereferences are intended to refer to like or corresponding parts, and inwhich:

FIG. 1 illustrates a diagram of a patient fall prediction system inaccordance with exemplary embodiments of the present invention;

FIG. 2 illustrates a system for processing video image data receivedfrom a patient fall prediction system according to an embodiment of thepresent invention;

FIG. 3 illustrates a flowchart of a method for determining bed fallcharacteristics according to an embodiment of the present invention;

FIG. 4 illustrates an exemplary motion detection according to anembodiment of the present invention;

FIG. 5 illustrates an exemplary centroid location according to anembodiment of the present invention;

FIG. 6 illustrates an image processed using connected componentsaccording to an embodiment of the present invention;

FIG. 7 illustrates an exemplary decision tree classifier trainedaccording to one embodiment of the invention;

FIG. 8 illustrates a flowchart of a method for analyzing fall alarmsaccording to an embodiment of the present invention; and

FIG. 9-10 illustrate exemplary false-alarm patterns according to anembodiment of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

In the following description, reference is made to the accompanyingdrawings that form a part hereof, and in which is shown by way ofillustration, specific embodiments in which the invention may bepracticed. These embodiments are described in sufficient detail toenable those skilled in the art to practice the invention, and it is tobe understood that other embodiments may be utilized. It is also to beunderstood that structural, procedural and system changes may be madewithout departing from the spirit and scope of the present invention.The following description is, therefore, not to be taken in a limitingsense. For clarity of exposition, like features shown in theaccompanying drawings are indicated with like reference numerals andsimilar features as shown in alternate embodiments in the drawings areindicated with similar reference numerals.

As will be appreciated by one of skill in the art, the present inventionmay be embodied as a method, system, or computer program product.Accordingly, the present invention may take the form of an entirelyhardware embodiment, an entirely software embodiment (includingfirmware, resident software, micro-code, etc.) or an embodimentcombining software and hardware aspects all generally referred to hereinas a “circuit” or “module.” Furthermore, the present invention may takethe form of a computer program product on a computer-usable storagemedium having computer-usable program code embodied in the medium.

Any suitable computer readable medium may be utilized. Thecomputer-usable or computer-readable medium may be, for example but notlimited to, an electronic, magnetic, optical, electromagnetic, infrared,or semiconductor system, apparatus, or device. More specific examples (anon-exhaustive list) of the computer-readable medium would include thefollowing: an electrical connection having one or more wires, a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), an optical fiber, a portable compact disc read-onlymemory (CD-ROM), an optical storage device, a transmission media such asthose supporting the Internet or an intranet, or a magnetic storagedevice. In the context of this document, a computer-usable orcomputer-readable medium may be any medium that can contain, store,communicate, propagate, or transport the program for use by or inconnection with the instruction execution system, apparatus, or device.The computer-usable medium may include a propagated data signal with thecomputer-usable program code embodied therewith, either in baseband oras part of a carrier wave. The computer usable program code may betransmitted using any appropriate medium, including but not limited tothe Internet, wireline, optical fiber cable, radio frequency (RF), etc.Moreover, the computer readable medium may include a carrier wave or acarrier signal as may be transmitted by a computer server includinginternets, extranets, intranets, world wide web, ftp location or otherservice that may broadcast, unicast or otherwise communicate anembodiment of the present invention. The various embodiments of thepresent invention may be stored together or distributed, eitherspatially or temporally across one or more devices.

Computer program code for carrying out operations of the presentinvention may be written in an object oriented programming language suchas Java, Smalltalk, or C++. However, the computer program code forcarrying out operations of the present invention may also be written inconventional procedural programming languages, such as the “C”programming language. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer. In the latter scenario, theremote computer may be connected to the user's computer through a localarea network (LAN) or a wide area network (WAN), or the connection maybe made to an external computer (for example, through the Internet usingan Internet Service Provider).

A data processing system suitable for storing and/or executing programcode may include at least one processor coupled directly or indirectlyto memory elements through a system bus. The memory elements can includelocal memory employed during actual execution of the program code, bulkstorage, and cache memories which provide temporary storage of at leastsome program code in order to reduce the number of times code must beretrieved from bulk storage during execution.

Input/output or I/O devices (including but not limited to keyboards,displays, pointing devices, etc.) can be coupled to the system eitherdirectly or through intervening I/O controllers.

Network adapters may also be coupled to the system to enable the dataprocessing system to become coupled to other data processing systems orremote printers or storage devices through intervening private or publicnetworks. Modems, cable modem and Ethernet cards are just a few of thecurrently available types of network adapters.

Throughout the specification and claims, terms may have nuanced meaningssuggested or implied in context beyond an explicitly stated meaning.Likewise, the phrase “in one embodiment” as used herein does notnecessarily refer to the same embodiment and the phrase “in anotherembodiment” as used herein does not necessarily refer to a differentembodiment. It is intended, for example, that claimed subject matterinclude combinations of exemplary embodiments in whole or in part.

FIG. 1 illustrates a diagram of a patient fall prediction system inaccordance with exemplary embodiments of the present invention. Asdepicted in the figure, patient fall prediction system 100 includespatient monitoring device 101 and nurse monitor device 110. Patientmonitoring device 101 captures video images of a portion of thepatient's room 120 via camera 102, which is coupled to camera controldevice 104. Camera 102 may be at least of medium quality, produce astable video output of 300 lines of resolution or greater and haveinfrared illumination or quasi night vision for operating in extremelylow light conditions. Additionally, video camera 102 may have arelatively fast shutter speed to capture relatively fast movementswithout blurring at frame rates of 20 fps or above. Camera controldevice 104 processes the video images received from camera 102 inaccordance with the novel fall prediction methodology discussed below.As such, camera control device 104 includes processor 106, memory 108and optional video processor 109. Camera control device 104 may be aspecial purpose device configured specifically for patient monitoring,such as the set-top control. In either case, memory 108 includes bothROM and RAM type as necessary for storing and executing fall predictionprogram instructions and a high capacity memory, such as a hard drivefor storing large sequences of video image frames.

Additionally, camera control device 104 may be fitted with a highcapacity flash memory for temporarily storing temporal image framesduring image processing and/or prior to more permanent storage on a harddrive or at a network location. Optional video processor 109 may be adedicated image processor under the control of an application routineexecuting on processor 106, or may be logic operating in processor 106.Under the fall prediction routines, video processor 109 analyzesportions of sequential images for changes in a particular area whichcorrelate to patient movements that are precursors to a fall. Patientmonitoring device 101 may be coupled to nurse monitor device 110 locatedin nurse's station 130 via distribution network 140, for transmittingsurveillance images of the patient's room and fall state information tonurse monitor device 110. Optionally, audible alarm 105 may be providedfor alerting healthcare professionals that camera control device 104 hasdetected that the patient is at risk of falling. Additionally, cameracontrol device 104 comprises other components as necessary, such asnetwork controllers, a display device and display controllers, userinterface, etc.

In many regards, nurse monitor device 110 may be structurally similar tocamera control device 104, however its primary functions are to set upthe fall prediction routines running at camera control device 104 and tomonitor fall state information and surveillance video provided bypatient monitoring device 101. Optimally, nurse monitor device 110 isconnected to a plurality of patient monitoring devices that are locatedin each of the patient rooms being monitored at the nurse station. Nursemonitor device 110 includes computer 112 coupled to display 114.Computer 112 may be a personal computer, laptop, net computer, or othernet appliance capable of processing the information stream. Computer 112further comprises processor 106, memory 108 and optional video processor109, as in camera control device 104, however these components functionquite differently. In setup phase, a healthcare professional views thepatient room setting and graphically defines areas of high risk for apatient fall, such as the patient bed, chair, shower, tub, toilet ordoorways. The graphic object may be manipulated on display 114 by usergestures using resident touch screen capabilities or the user gesturesmay be entered onto a display space using mouse 116 or other type userinterface through a screen pointer (not shown). Exemplary patient roomsfrom a viewpoint perspective of a video image are described more fullywith respect to FIGS. 4A and 4B of commonly-owned U.S. Pat. No.9,041,810, the description of which is incorporated herein by reference.That information is passed on to patient monitoring device 101 whichmonitors the selected area for motion predictive of a movement that is aprecursor to a patient fall. When patient monitoring device 101 detectsthat the patient is at high risk of falling, the fall state isimmediately transmitted to nurse monitor device 110, which prioritizesthe information over any other routine currently running as an alarm.This is accompanied by an audible alarm signal (via audible alarm 105).The healthcare provider can then take immediate response action toprevent a patient fall.

In accordance with other exemplary embodiments of the present invention,patient monitoring device 101 may operate independently, as aself-contained, standalone device. In that case, patient monitoringdevice 101 should be configured with a display screen and user interfacefor performing setup tasks. Audible alarm 105 would not be optional. Inaccordance with still another exemplary embodiment, patient monitoringdevice 101 may comprise only video camera 102, which is coupled to nursemonitor device 110 at a remote location. In operation, camera 102transmits a stream of images to nurse monitor device 110 for videoprocessing for fall prediction. It should be appreciated, however, thatoften high volume traffic on distribution networks, such as sequences ofvideo images, experience lag time between image capture and receipt ofthe images at the remote location. To avoid undesirable consequencesassociated with lag, the distribution network bandwidth should besufficiently wide such that no lag time occurs, or a dedicated videopath be created between nurse monitor device 110 and patient monitoringdevice 101. Often, neither option is practical and therefore, the videoprocessing functionality is located proximate to video camera 102 inorder to abate any undesirable lag time associated with transmitting theimages to a remote location.

In addition, patient fall prediction system 100 may comprise adeactivator for temporarily disabling the patient fall prediction systemunder certain conditions. In the course of patient care, healthcareprofessionals move in and out of patient rooms and in so doing, solicitmovements from the patients that might be interpreted as a movement thatprecedes a patient fall by the patient fall prediction system.Consequently, many false-alarms may be generated by the mere presence ofa healthcare professional in the room. One means for reducing the numberof false-alarms is to temporarily disarm the patient fall predictionsystem whenever a healthcare professional is in the room with a patient.Optimally, this is achieved through a passive detection subsystem thatdetects the presence of a healthcare professional in the room, using,for example, RFID or FOB technology. To that end, patient monitoringdevice 101 will include receiver/interrogator 107 for sensing an RFIDtag or FOB transmitter. Once patient monitoring device 101 recognizes ahealthcare professional is in the proximity, the patient fall predictionsystem is temporarily disarmed. The patient fall prediction system canautomatically rearm after the healthcare professional has left the roomor after a predetermined time period has elapsed. Alternatively, thepatient fall prediction system may be disarmed using a manual interface,such as an IR remote (either carried by the healthcare professional orat the patient's bedside) or a dedicate deactivation button, such as atcamera control device 104 or in a common location in each of the rooms.In addition to the local disarming mechanisms, the patient fallprediction system may be temporarily disarmed by a healthcareprofessional at care station 130 using computer 112 prior to enteringthe patient's room.

According to another embodiment, the patient fall prediction system mayuse accelerometers on RFID tags worn by patients to reduce false-alarmsdetected by the patient fall prediction system. A movement or eventinterpreted as a fall may be compared with accelerometer informationthat are consistent with an actual fall or a false-alarm. For example,the patient fall prediction system may indicate that the patient movedand may have fallen out of bed, but the accelerometer worn by thepatient doesn't show sufficient acceleration for such an event and maythen conclude that the event is a false-alarm.

In operation, patient fall prediction system 100 operates in two modes:setup mode and patient monitoring mode. A setup method implementing apatient fall prediction system for detecting patient movements isdescribed more fully with respect to FIG. 5 of commonly-owned U.S. Pat.No. 9,041,810, the description of which is incorporated herein byreference. Additionally, the creation of a virtual bedrail on a displayin the setup mode is described more fully with respect to FIGS. 6A-6D,7A and 7B of commonly-owned U.S. Pat. No. 9,041,810, the description ofwhich is incorporated herein by reference.

FIG. 2 illustrates a system for processing video image data receivedfrom a patient fall prediction system 100 according to an embodiment ofthe present invention. As the embodiment of FIG. 2 illustrates, a system200 comprises a patient monitor device 202 and a nurse monitor device212, as discussed supra. The system 200 further includes a video storageand retrieval device 204 for receiving video frame data from the patientmonitor device 202 and storing said data. In one embodiment, video framedata may be stored permanently, or, alternatively, may be storedtemporarily solely for processing. Video frame data may be stored in anumber of formats and on a number of mechanisms such as flat filestorage, relational database storage, or the like.

Classifier 206, training system 208, feature definition storage 210, andalarm verifier 214 are interconnected to train and operate theclassifier 206, as discussed in more detail below. In one embodiment,classifier 206 and training system 208 may comprise a dedicated server,or multiple servers, utilizing multiple processors and designed toreceive and process image data using techniques described herein.Likewise, feature definition storage 210 may comprise a dedicated memoryunit or units (e.g., RAM, hard disk, SAN, NAS, etc.).

Feature definition storage 210 may store a predefined number offeatures, and an associated process for extracting such features fromthe data stored within video storage and retrieval device 204. Exemplaryfeatures are discussed more fully with respect to FIGS. 3 through 7. Thetraining system 208 loads features from the feature definition storage210 and extracts and stores features from the video received from videostorage and retrieval device 204. Using techniques discussed more fullyherein, the training system 208 processes a plurality of frames andgenerates a classifier 206. The classifier 206 may be stored forsubsequent usage and processing of additional video frames.

In operation, classifier 206 receives video data from video storage andretrieval device 204. As discussed with respect to FIG. 2, theclassifier 206 analyzes incoming video frames and extracts features fromthe video frames. Using these extracted features, the classifier 206executes a supervised learning process to classify a given frame ascausing an alarm or not causing an alarm, as exemplified in FIG. 7.After classifying a given frame, the classifier 206 may then transmitthe results of the classification to the nurse monitor device 212.

In one embodiment, the classifier 206 may transfer data indicating thatan alarm condition should be raised at the nurse monitor device 212. Thedata indicating the alarm condition may be confirmed or verified byalarm verifier 214. Alarm verifier 214 may be loaded with a set ofpredetermined patterns of motion features associated with risk andnon-risk events used to aid in improving the accuracy of the classifier206. In particular, alarm verifier 214 can be utilized to minimizefalse-alarms and confirm real alarms made by the classifier 206.Features extracted from the video frames by classifier 206 may becompared with the feature patterns stored in alarm verifier 214.

Additionally, the classifier 206 may provide a feedback loop via alarmverifier 214 to the training system 208. Using this loop, the classifier206 may continuously update the training data set used by trainingsystem 208. In alternative embodiments, the classifier 206 may onlyupdate the training data set in response to a confirmation that an alarmcondition was properly raised. For example, the alarm verifier 214(and/or nurse monitor device 212) may be configured to confirm or refutethat an actual alarm condition has been properly raised. In this manner,the classifier 206 updates the predicted alarm condition based onverification data from alarm verifier 214 and supplements the trainingsystem 208 with the corrected data.

Although illustrated as separate from the nurse monitor device 212, theclassifier 206, training system 208, feature definition storage 210, andthe alarm verifier 214 may alternatively be located locally at the nursemonitor device 212. Further, FIG. 2 illustrates a single classifier 206,single training system 208, and single feature definition storage 210,however additional embodiments may exist wherein the system 200 utilizesmultiple classifiers, training systems, and feature definition storageunits in order to increase throughput and/or accuracy of the system 200.

FIG. 3 presents a flowchart of a method for determining bed fallcharacteristics according to an embodiment of the present invention. Acomputing system may receive surveillance video including a plurality ofvideo frames and a log of events or alarms associated with bed fallevents. Alarm cases are identified from video, step 302. Each video canbe examined and labeled as alarm or no-alarm cases. In one embodiment,the identification of alarm cases may be based upon historical dataassociated with the video. For example, as discussed supra a fallprediction system may be configured to capture video frames and triggeralerts based on identified motion as described in commonly-owned U.S.Pat. No. 9,041,810. Alternatively, the method 300 may utilizeunsupervised clustering techniques to automatically label video. Thecorrelation between video and alarms may be stored for further analysis,thus associated a video, including a plurality of frames, with an alarmcondition. Thus, the method 300 may access a database of video data andselect that video data that has been known to trigger an alarm.

After identifying a video that has triggered an alarm, the specificframes that trigger the alarm case are determined, step 304, and videoframes that include alarm cases or events related to fall risks may becollected. In one embodiment, the number of videos that correspond to analarm case may be greater than the number of videos that actuallycorrespond to a potential fall, given the potential for false positivesas discussed supra. Furthermore, a given video may have potentiallytriggered multiple alarms during the course of the video. In oneembodiment, false positives may be further limited by requiring threeconsecutive alarms before signaling an alert. According to anotherembodiment, false positives may be reduced by corroborating the videoswith accelerometer information (e.g., checking accelerometer worn bypatient corresponding to the time of video frames including alarm casesor events). Thus, step 304 operates to identify, as narrowly aspossible, the specific video frames corresponding to a given alarm. Inone embodiment, the number of frames needed to identify the instance analarm is triggered is three, although the number of frames required maybe increased or decreased. By utilizing multiple prior frames, themethod 300 may compensate for changes in lighting or other factors thatcontribute to a noise level for a given set of frames.

For each alarm case, the number and sequence of frames that couldtrigger an alarm for bed fall are identified. In an alternativeembodiment, video and frames may be manually tagged and received fromstaff or an operator of a video surveillance system. Additionally, themethod 300 may also tag those video frames that do not trigger an alarm,to further refine the supervised learning approach. By identifyingframes that do not trigger an alarm, the method 300 may increase thereliability of the system versus solely tagging those frames that docause an alarm.

For each set of frames and associated alarm cases, the method 300detects motion pixels in the alarm triggering frames, step 306.Detecting motion may include comparing, pixel by pixel, between acurrent frame and at least one previous frame. In some embodiments,multiple, previous frames may be selected to reduce noise. For example,at least two previous frames F₁ and F₂ are selected to be compared witha current frame F₃. Each pixel of F₁ and F₂ may be selected and comparedwith corresponding pixels in F₃. Thus, in the illustrated embodiment,the method compares, pixel by pixel, the change of values of each pixelto determine when a pixel “changes,” thus indicating a type of motion.Detecting motion in frames may comprise creating a binary motion imageillustrated in FIG. 4.

Motion features are determined from the motion pixels, step 308. Motionfeatures or a set of derived values relating to the motion of a virtualbed zone may be extracted. In one embodiment, a virtual bed zone maycomprise a virtual zone delineated by virtual bed rails or virtual chairrails. Motion features may include a centroid, centroid area, bed motionpercentage, connected components, and unconnected motion features. Eachof these features is discussed in more detail below.

A first motion feature that may be detected is a “centroid” feature. Inone embodiment, a centroid is the weighted average x and y coordinatesof all motion pixels and can be thought of as the “center of mass” ofthe motion analysis. Thus if there are two areas of identical motion,the centroid feature will indicate an area between the two areas on boththe x- and y-axes as the centroid, or center of mass, area. Such amotion feature indicates the primary locus of movement which may beuseful in determining whether motion is near a fall risk area (e.g., theedge of a bed) or, on average, not near a fall risk area. An exemplarycentroid feature is illustrated in more detail with respect to FIG. 5.

A second motion feature that may be detected is a “centroid area”feature. In one embodiment, the centroid area feature is the count ofall motion pixels in the image. Thus, the centroid area featurerepresents that total movement between frames. A small centroid areafeature indicates little movement, while a large centroid area featureindicates substantial movement. In one embodiment, a number of pixels ina motion image (e.g., as illustrated in FIGS. 4 and 5) may be counted.

A third motion feature that may be detected is a “bed motion percentage”feature. The bed motion percentage feature corresponds to the ratio ofmotion pixels within a plurality of defined virtual bed zones to thetotal pixel count in the same virtual bed zones. As described more fullyin U.S. Pat. No. 9,041,810, a virtual bed zone may be created utilizingdefined boundaries programmatically determined for a given set of imageframes. In one example, a virtual bed zone may simply be a perimeteraround a bed, while more involved virtual bed zones may be utilized. Thebed motion percentage feature represents the amount of movementlocalized to the bed zone and thus indicates whether there issubstantial movement with a bed zone. The bed motion percentage featureis illustrated with respect to FIG. 5.

A fourth motion feature that may be detected is a “connected components”feature. This feature corresponds to the number of “connected” pixelsnear a bed zone. In one embodiment, the illustrative method first“groups” pixels that are within a certain distance from each other, thusforming “connected” groups of pixels, versus individual pixels. For eachof these groups of pixels, the method 300 may ignore those groups thatare not within a specified distance from an identified bed zone (e.g.,the edge of a bed). In one embodiment, the connected components comprisethe number of remaining components. In alternative embodiments, thefeature may be further refined to compute the ratio of the remainingmotion outside the bed zone to all motion inside the bed zone asrepresented by the components.

A fifth motion feature that may be detected is an “unconnected motion”feature, a feature related to the connected motion feature. In oneembodiment, this feature calculates the amount of motion in the centroidarea (as discussed supra) that cannot be attributed to the motion withinand near the bed zone using the connected components discussed supra.

The connected components and unconnected motion features are illustratedwith respect to FIG. 6. While the present disclosure only discussed fivefeatures, in alternative embodiments, additional features may beutilized to refine the accuracy of the method 300.

After identifying each of these features, a training data set may beconstructed with each of the features being associated with a set offrames and a label indicating that an alarm was, or was not triggered. Aclassifier, such as a decision tree or similar learning machine (such asnearest neighbor, support vector machines, or neural networks), istrained based on the features, step 310. In one embodiment, the method300 may input the training data set into a decision tree classifier toconstruct a decision tree utilizing the identified features. Anexemplary resulting decision tree is depicted in FIG. 7.

A classifier may be chosen for training based on a training set of thefeatures determined from the motion images and the identification ofalarm cases for certain video frames. Any classifier may be selectedbased on its ease of training, implementation, and interpretability. Inone embodiment, the method 300 may utilize ten-fold cross-validation toconstruct a decision tree. During testing, the use of cross-validationwas shown to accurately classify unknown frames as alarm or no-alarmconditions approximately 92% of the time using the five features above.Although the method 300 discusses a single classifier, alternativeembodiments existed wherein a collection of classifiers (e.g., decisiontrees) may be utilized to provide higher accuracy than a singleclassifier. For example, the method 300 may employ boosted decisiontrees or a random forest to maximize accuracy.

After the classifier is trained, it may be utilized in a productionsetting. In one embodiment, the classifier may be employed in thepatient fall prediction system discussed supra. That is, the classifiermay be used in place of existing techniques for analyzing image frames.In an exemplary embodiment, the fall prediction system may feed videoframes into the classifier on a real-time or near real-time basis. Asdiscussed more fully with respect to FIG. 7, the method 300 may generatea fall alarm based on the output of the classifier, step 312. Theclassifier may include various nodes for facilitating a fall detectionsystem to determine whether a given unclassified frame of video shouldtrigger an alarm associated with a fall risk event.

FIG. 4 illustrates the results of comparing, pixel-by-pixel, themovement in a frame 403 as compared to two previous frames 401 and 402.Specifically, the embodiment in FIG. 4 illustrates three frames showinga patient 410 at first stationary in a bed (frame 401) next to a table408, reaching for a table (frame 402), and moving the table closer tothe bed (frame 403). Each frame additionally includes a virtual bed zone412 that roughly corresponds to the shape of the bed (not illustrated).Note that the embodiment of FIGS. 4 through 6 illustrate a top-down viewof a patient, however alternative embodiments exist wherein a camera maybe placed in other positions.

In order to create a motion image 404, as discussed, the method 300compares frames 401 and 402 to frame 403. If the value of a pixel in acurrent frame 403 has changed (e.g., beyond a certain threshold) fromthe two previous frames 401 and 402, it may be marked as a motion pixel.This may be repeated for all of the pixels in the current frame toobtain a set of motion pixels, including representative motion pixel414. A resulting motion image 404 (which may be a binary graph) may beconstructed whose values are zero everywhere except for those pixelsthat differ from both prior frames by more than some threshold (thisvalue can be chosen by optimizing the error on a resulting classifier).Accordingly, a difference in both prior frames 401 and 402, the systemis able to filter some of the noise due to changes in lighting, etc.Motion pixels from the motion image may be used to engineer features forallowing a machine learning algorithm to separate alarm from no-alarmframes.

As illustrated in FIG. 4, a resultant motion image 404 illustrates areaswhere no motion has occurred (white) and where motion has been detectedin the past two frames (shaded). Specifically, as exemplified in FIG. 4,motion is detected near the patient's right hand 406 which correspondsto the patient's movement. Further, the Figure illustrates the movementof a non-patient object 408 (i.e., table) closer to the virtual bedzone. As discussed supra, the number of light pixels in motion image 404may counted to calculate the centroid area of a frame 403.

FIG. 5 illustrates an exemplary centroid location according to anembodiment of the present invention. Video frame 502 and motion image504 illustrate a subject within bounding virtual bed zone. As discussedsupra, motion image 504 may be constructed for frame 502 based onprevious frames and illustrates the movement leading up to frame 502.Note that FIG. 5 illustrates motion pixels in motion image 504 as shadedpixels. As discussed supra, a centroid 506 may be a located bycalculating a weighted average of x- and y-coordinates of all the motionpixels in motion image 504. FIG. 5 illustrates the effect of thelocation of motion pixels on the centroid 506 location. As illustratedin motion image 504, the sparse motion pixels associated with thepatient are offset by the dense motion pixels focused around table 508.Since the centroid feature is based on the number of motion pixels and,importantly, their position, the centroid is located approximately inthe center of all motion detected in the motion image 504. FIG. 5further illustrates a virtual bed zone 510. As discussed supra, thevirtual bed zone 510 may be utilized to calculate the bed motionpercentage by providing a bounding area in which to count the number ofmotion pixels.

FIG. 6 presents an image processed using connected components accordingto an embodiment of the present invention. A connected componentsfeature may be determined for motion pixel images of a plurality offrames as discussed more fully with respect to FIG. 4. Motion pixelsthat are connected (e.g., adjacent) may be grouped in clusters andmotion pixel groups that don't have at least one pixel within somethreshold distance of the virtual bed zone are pruned away from the fullmotion image 602 resulting in a connected components image 604 asillustrated by near/inside rails motion in FIG. 6. As illustrated, image604 only contains those pixels within the virtual bed zone or within aspecified distance from the bed zone.

The ratio of the remaining motion pixels outside the virtual bed zone toall motion pixels inside the virtual bed zone may then be computed todetermine a connected components ratio. Unconnected motion may furtherbe determined by calculating the amount of motion (pixels) in thecentroid area that is unrelated to the motion within and near thevirtual bed zone using the connected components above.

FIG. 7 presents an exemplary decision tree classifier 700 trainedaccording to one embodiment of the invention. As discussed supra, themethod 300 may generate a classifier such as the exemplary decision treedepicted in FIG. 7. In production, the decision tree classifier 700receives a plurality of frames, creates a motion image, and calculates anumber of features discussed more fully above. After generating thefeatures for the plurality of frames, the method 300 may utilize adecision tree classifier such as that illustrated in FIG. 7. Notably,the decision tree classifier illustrated in FIG. 7 is exemplary only andactual decision tree classifiers utilized may differ in complexity orthe features/values utilized.

As illustrated in FIG. 7, a decision tree classifier 700 first analyzesthe feature to determine if the bed motion percentage feature 702 has avalue above 5.49. If the value of this feature is greater than 5.49, thedecision tree classifier 700 then determines if the unconnected motionfeature 706 is greater than 0.3. If so, the decision tree classifier 700indicates that the incoming video frames are associated with an alarm714. In one embodiment, the decision tree classifier 700 may beconfigured to automatically trigger an alarm indicating a potential fallas discussed supra. Alternatively, if the decision tree classifier 700determines that the unconnected motion feature 706 is below or equal to0.3, the decision tree classifier 700 may then determine the value ofthe connected components feature 712. If the connected componentsfeature 712 is above 0.1, the decision tree classifier 700 indicatesthat no alarm condition exists 722. Alternatively, if the connectedcomponents feature 712 is lower than or equal to 0.1, the decision treeclassifier 700 raises an alarm 720.

Returning to the top of FIG. 7, the decision tree classifier 700 mayalternatively determine that the bed motion percentage 702 is below orequal to 5.49. In this instance, the decision tree classifier 700 maythen determine whether the centroid area 704 is greater than 965 or lessthan or equal to 965. If the centroid area 704 is above 965, an alarmcondition may be triggered 710. If not, the decision tree classifier 700may then analyze the centroid feature 708 to determine if the value isabove 0.29 or below (or equal to) 0.29. A centroid value above 0.29 maytrigger an alarm condition 718, while a value less than or equal to 0.29may not 716.

FIG. 8 presents a flowchart of a method for analyzing fall alarmsaccording to an embodiment of the present invention. The decision treeclassifier may be improved to minimize false-alarms and/or confirmingreal alarms made by the classifier. Improvements to the decisions of theclassifier may be made by generating and storing a set of predeterminedpatterns of motion features which have been determined to be either riskor non-risky. Examples of exceptions or false-alarms include accountingfor individuals other than the patient in bed, such as, a healthcareprovider walking into the room and past the bed, or back out the door,or visitors getting up off the adjacent chair, or the like. Examples ofclear risks include patients rolling over near the edge of the bed,moving a left off the bed, as if to stand, etc. A pattern of motionfeatures characterizing these examples can be identified andappropriately stored as false-alarms and real alarms for an alarmverifier. These patterns may be created by recording the pattern ofmotion features associated with the real alarm and false-alarm cases ina lookup table, database or similar storage.

Alarm cases from video are identified, step 802. Each video can beexamined and labeled as real alarm or false-alarm cases. Video data froma database of recorded video that are known to trigger or indicatereal-alarms and false-alarms, may be accessed and selected. In oneembodiment, the identification of alarm cases may be based uponhistorical data associated with the video. Alternatively, the method mayutilize unsupervised clustering techniques to automatically label video.Identifying alarm cases includes receiving a plurality of frames thatinclude examples of real alarms and false-alarms. The plurality offrames may be identified, labeled or tagged with metadata to indicate aparticular real alarm or false-alarm case. Frames that trigger an alarmcase are determined, step 804. The plurality of frames can be narrowedand reduced to only frames that include relevant real alarm orfalse-alarm movements. Motion pixels in the frames are detected, step806, to create motion images for the plurality of frames.

Motion features patterns are determined based on the motion pixels, step808. Motion features relating to the motion of a virtual bed zone aredetermined for each real alarm or false-alarm case from the plurality offrames. A motion feature may be any one of centroids, centroid area, bedmotion percentage, connected components, and unconnected motioncalculations, as described above. According to an alternativeembodiment, determining motion features for the plurality of frames isperformed in the training of the classifier, and as such, the motionfeatures may be saved in the training system (in such a case, steps 804and 806 may be bypassed). Once motion features are determined, patternsof the motion features are determined for each of the real alarm orfalse-alarm case. The motion feature patterns may then be stored to analarm verifier for comparison with the classifier, step 810.Particularly, each case may be associated with a pattern of one or moremotion features characteristic of a given fall or non-fall event. Thealarm verifier may contain a plurality of such patterns for comparisonwith fall alarms generated by the classifier of the patient falldetection system according to embodiments of the present invention.

A fall alarm is received from a classifier of a patient fall detectionsystem, step 812. The fall alarm may be generated by the classifieridentifying a plurality of real-time video frames that trigger a bedfall alarm from a production setting. The stored motion feature patternsare compared with the fall alarm, step 814. Fall alarms generated by theclassifier can be confirmed or verified by comparing motion features ofvideo frames corresponding to the fall alarms with the stored motionfeature patterns of false-alarms and real alarms. According to otherembodiments, steps 802 through 814 may be performed with respect toaccelerometer information known to trigger or indicate real-alarms andfalse-alarms instead of or in addition to the video data.

FIG. 9 presents an exemplary stored false-alarm pattern. The fall alarmpattern may characterize a series of images over time (e.g., Time 0-Time4) in a motion profile. The motion profile can be derived from a featureof difference to determine motion shape and direction from a centroidlocation over time. The feature of difference is determined from acaptured image and a difference image. The feature of difference couldbe centroid of differences, geometric pattern found in difference,center of bounding box of differences or any other feature that istrackable by position.

FIG. 10 presents additional exemplary stored false-alarm patterns. Theillustrated patterns include a series of images, centroid of difference,and a motion profile. Pattern 1002 shows a pattern of a person (nurse orfamily) arriving at the bed and breaking the barrier going away from thebed. Pattern 1004 illustrates a pattern of someone sitting at the bed ontop or across the rail. When they move away, it could look like someonemoving off the bed (a pattern that would look like a fall) when inreality it is a chair moving back from the bed to its normal location.Pattern 1006 illustrates a pattern of a patient sitting well back on hisbed with his legs across the edge. That he is swinging his legs back inforth might be an indication that he is just sitting there. Pattern 1008illustrates a tray/table that can be moved back and forth on the railand also in and out (notice the wavy line). This might cause a falsepositive because it is moving from over the bed, off the bed, however,the wavy pattern can help identify that it is a tray/table.

Comparing a fall alarm to patterns of motion features stored in thealarm verifier includes finding statistically significant similaritiesbetween the fall alarm (motion features of corresponding video frames)and the stored patterns of motion features. The comparing may furtherinclude performing similarity analysis to determine statisticallysignificant (i.e., not random) matches with the patterns of the alarmverifier. Similarity analysis may include comparing patterns of pixelsto find similarities in at least one of centroid features, centroidarea, bed motion percentage, connected components, and unconnectedmotion calculations. The analysis may also compare distances anddetermine correlations between centroid locations, unconnected motion,connected components, etc. For example, the method may compare centroiddistances from motion pixels of a plurality of frames corresponding toan alarm with centroid distances from motion pixels of one or more realalarm cases over a plurality of frames and verify the alarm whencentroid distances are determined to match by a predetermined centroiddistance threshold. Correlation between centroid locations includesstatistical relationships involving dependence for finding a predictiverelationship among the motion features. Additionally, regressionanalysis may be used to determine relationships among variables (of themotion features). Further description and details of regression analysismay be found in U.S. Patent Application Publication No. 2014/0304213,entitled “REGRESSION ANALYSIS SYSTEM AND REGRESSION ANALYSIS METHOD THATPERFORM DISCRIMINATION AND REGRESSION SIMULTANEOUSLY” which is hereinincorporated by reference in its entirety.

Referring back to FIG. 8, the fall alarm is confirmed based on thecomparison, step 816. Matching of the stored patterns with the fallalarm can verify or confirm the fall alarm. Additionally, theconfirmation of a fall alarm may be used as feedback into training theclassifier. The confirmation information may be forwarded to thetraining system as a second stage machine learning, where the classifierlearns preliminary concepts in the first stage and live identificationresults (fall alarms) of the classifier may then be used as trainingdata in conjunction with confirmation of the fall alarms for a secondstage machine learning. Second stage machine learning are described infurther detail in U.S. Patent Application Publication No. 2009/0228413,entitled “LEARNING METHOD FOR SUPPORT VECTOR MACHINE” and U.S. PatentApplication Publication No. 2006/0248031, entitled “METHOD FOR TRAININGA LEARNING-CAPABLE SYSTEM” which are herein incorporated by reference intheir entirety.

FIGS. 1 through 8 are conceptual illustrations allowing for anexplanation of the present invention. Notably, the figures and examplesabove are not meant to limit the scope of the present invention to asingle embodiment, as other embodiments are possible by way ofinterchange of some or all of the described or illustrated elements.Moreover, where certain elements of the present invention can bepartially or fully implemented using known components, only thoseportions of such known components that are necessary for anunderstanding of the present invention are described, and detaileddescriptions of other portions of such known components are omitted soas not to obscure the invention. In the present specification, anembodiment showing a singular component should not necessarily belimited to other embodiments including a plurality of the samecomponent, and vice-versa, unless explicitly stated otherwise herein.Moreover, applicants do not intend for any term in the specification orclaims to be ascribed an uncommon or special meaning unless explicitlyset forth as such. Further, the present invention encompasses presentand future known equivalents to the known components referred to hereinby way of illustration.

The foregoing description of the specific embodiments will so fullyreveal the general nature of the invention that others can, by applyingknowledge within the skill of the relevant art(s) (including thecontents of the documents cited and incorporated by reference herein),readily modify and/or adapt for various applications such specificembodiments, without undue experimentation, without departing from thegeneral concept of the present invention. Such adaptations andmodifications are therefore intended to be within the meaning and rangeof equivalents of the disclosed embodiments, based on the teaching andguidance presented herein. It is to be understood that the phraseologyor terminology herein is for the purpose of description and not oflimitation, such that the terminology or phraseology of the presentspecification is to be interpreted by the skilled artisan in light ofthe teachings and guidance presented herein, in combination with theknowledge of one skilled in the relevant art(s).

While various embodiments of the present invention have been describedabove, it should be understood that they have been presented by way ofexample, and not limitation. It would be apparent to one skilled in therelevant art(s) that various changes in form and detail could be madetherein without departing from the spirit and scope of the invention.Thus, the present invention should not be limited by any of theabove-described exemplary embodiments, but should be defined only inaccordance with the following claims and their equivalents.

What is claimed is:
 1. A surveillance system for detecting a fall riskcondition, the system comprising: a computer system comprising memoryand logic circuitry configured to: retrieve, from a storage device,video data that is associated with triggering false alarm cases ofpatient falls; determine motion feature patterns that are associatedwith risk or non-risk events from the video data; classify, using aclassifier that is trained with a set of features associated withtriggering alarms for patient falls, one or more of a plurality offrames generated from a surveillance camera as causing an alarm; verifythe classification by extracting motion feature patterns of the one ormore of the plurality of frames and comparing the extracted motionfeature patterns with the determined motion feature patterns; andgenerate feedback to the classifier with training data based on theverification of the alarm.
 2. The surveillance system of claim 1 whereinthe video data includes a log of events.
 3. The surveillance system ofclaim 1 further comprising the computer system determining real-alarmcases of patient falls from the video data based on triggering ofconsecutive alarms.
 4. The surveillance system of claim 1 furthercomprising the computer system receiving graphically defined areas tomonitor for patient falls through a user interface.
 5. The surveillancesystem of claim 4 wherein the determined motion feature patternscorrespond to motion of a virtual bed zone that is generated based onthe graphically defined areas.
 6. The surveillance system of claim 1wherein the training data includes the determined motion featurepatterns.
 7. The surveillance system of claim 1 wherein the motionfeature patterns include at least one of a centroid location, centroidarea, connected components ratio, bed motion percentage, and unconnectedmotion.
 8. A method for detecting a fall risk condition, the methodcomprising: retrieving, by a computer system from a storage device,video data that is associated with triggering false alarm cases ofpatient falls; determining, by the computer system, motion featurepatterns that are associated with risk or non-risk events from the videodata; classifying, by the computer system using a classifier that istrained with a set of features associated with triggering alarms forpatient falls, one or more of a plurality of frames generated from asurveillance camera as causing an alarm; verifying, by the computersystem, the classification by extracting motion feature patterns of theone or more of the plurality of frames and comparing the extractedmotion feature patterns with the determined motion feature patterns; andgenerating, by the computer system, feedback to the classifier withtraining data based on the verification of the alarm.
 9. The method ofclaim 8 wherein the video data includes a log of events.
 10. The methodof claim 8 further comprising determining real-alarm cases of patientfalls from the video data based on triggering of consecutive alarms. 11.The method of claim 8 further comprising receiving graphically definedareas to monitor for patient falls through a user interface.
 12. Themethod of claim 11 wherein the determined motion feature patternscorrespond to motion of a virtual bed zone that is generated based onthe graphically defined areas.
 13. The method of claim 8 wherein thetraining data includes the determined motion feature patterns.
 14. Themethod of claim 8 wherein the motion feature patterns include at leastone of a centroid location, centroid area, connected components ratio,bed motion percentage, and unconnected motion.
 15. Non-transitorycomputer-readable media comprising program code that when executed by aprogrammable processor causes execution of a method for detecting a fallrisk condition, the computer-readable media comprising: computer programcode for retrieving from a storage device, video data that is associatedwith triggering false alarm cases of patient falls; computer programcode for determining motion feature patterns that are associated withrisk or non-risk events from the video data; computer program code forclassifying, using a classifier that is trained with a set of featuresassociated with triggering alarms for patient falls, one or more of aplurality of frames generated from a surveillance camera as causing analarm; computer program code for verifying the classification byextracting motion feature patterns of the one or more of the pluralityof frames and comparing the extracted motion feature patterns with thedetermined motion feature patterns; and computer program code forgenerating feedback to the classifier with training data based on theverification of the alarm.
 16. The non-transitory computer-readablemedia of claim 15 further comprising computer program code fordetermining real-alarm cases of patient falls from the video data basedon triggering of consecutive alarms.
 17. The non-transitorycomputer-readable media of claim 15 further comprising computer programcode for receiving graphically defined areas to monitor for patientfalls through a user interface.
 18. The non-transitory computer-readablemedia of claim 17 wherein the determined motion feature patternscorrespond to motion of a virtual bed zone that is generated based onthe graphically defined areas.
 19. The non-transitory computer-readablemedia of claim 15 wherein the training data includes the determinedmotion feature patterns.
 20. The non-transitory computer-readable mediaof claim 15 wherein the motion feature patterns include at least one ofa centroid location, centroid area, connected components ratio, bedmotion percentage, and unconnected motion.